LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.

The industry-standard open-source ecosystem for high-fidelity deep learning face synthesis and VFX.
FaceSwap-Branches represents the evolved, community-driven ecosystem of the original FaceSwap project, which remains the premier open-source multi-platform deep-learning face-swapping software. In 2026, the architecture has transitioned toward a highly modular, plugin-based system that supports a variety of neural network backends, including TensorFlow, PyTorch, and specialized GAN (Generative Adversarial Network) branches. The platform is designed for researchers, VFX artists, and privacy advocates who require granular control over the data pipeline—from extraction and alignment to training and conversion. Unlike black-box SaaS tools, FaceSwap-Branches allows for precise adjustment of loss functions, masking algorithms (such as XSeg and BiSeNet), and hardware optimization via NVIDIA CUDA, AMD ROCm, or Apple Silicon's Metal. As of 2026, it occupies a critical niche in the market by providing a zero-cost, privacy-focused alternative to commercial deepfake services, often used in professional film production for de-aging and localized marketing content creation. Its technical depth, supported by an exhaustive community wiki, ensures it remains the baseline for academic research into synthetic media and digital forensics.
Supports MTCNN, S3FD, and RetinaFace for high-precision facial landmark detection.
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Photorealistic 4k upscaling via iterative latent space reconstruction.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A neural network-based masking tool that allows users to 'paint' masks and train the AI to recognize obstructions.
Integration of Discriminator networks to enhance high-frequency detail (skin pores, hair).
On-the-fly color matching using Lab, RDE, or Seamless cloning methods during conversion.
Support for NVIDIA (CUDA), AMD (ROCm), and Intel (OpenVINO) via a unified abstraction layer.
Real-time warping, rotation, and lighting shifts during training to improve model generalization.
Automated generation of preview frames at set iterations to track training progress visually.
Reducing the cost of traditional frame-by-frame digital retouching for younger versions of actors.
Registry Updated:2/7/2026
Convert with Lab color matching
Protecting witness identity while maintaining facial expressions and emotional impact.
Adapting a global celebrity's face to appear in regional-specific clothing or settings without a reshoot.